/*=========================================================================
 *
 *  Copyright Insight Software Consortium
 *
 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
 *  You may obtain a copy of the License at
 *
 *         http://www.apache.org/licenses/LICENSE-2.0.txt
 *
 *  Unless required by applicable law or agreed to in writing, software
 *  distributed under the License is distributed on an "AS IS" BASIS,
 *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  See the License for the specific language governing permissions and
 *  limitations under the License.
 *
 *=========================================================================*/

#include "itkAmoebaOptimizer.h"
#include "vnl/vnl_vector_fixed.h"
#include "vnl/vnl_vector.h"
#include "vnl/vnl_matrix.h"
#include "itkMath.h"
#include <iostream>

/**
 *  The objective function is the quadratic form:
 *
 *  1/2 x^T A x - b^T x
 *
 *  Where A is represented as an itkMatrix and
 *  b is represented as a itkVector
 *
 *  The system in this example is:
 *
 *     | 3  2 ||x|   | 2|   |0|
 *     | 2  6 ||y| + |-8| = |0|
 *
 *
 *   the solution is the vector | 2 -2 |
 *
 *   and the expected final value of the function is 10.0
 *
 * \class amoebaTestF1
 */
class amoebaTestF1 : public itk::SingleValuedCostFunction
{
public:

  using Self = amoebaTestF1;
  using Superclass = itk::SingleValuedCostFunction;
  using Pointer = itk::SmartPointer<Self>;
  using ConstPointer = itk::SmartPointer<const Self>;
  itkNewMacro( Self );
  itkTypeMacro( amoebaTestF1, SingleValuedCostFunction );

  enum { SpaceDimension=2 };

  using ParametersType = Superclass::ParametersType;
  using DerivativeType = Superclass::DerivativeType;
  using MeasureType = Superclass::MeasureType;

  using VectorType = vnl_vector<double>;
  using MatrixType = vnl_matrix<double>;


  amoebaTestF1():m_A(SpaceDimension,SpaceDimension),m_B(SpaceDimension)
   {
    m_A[0][0] =  3;
    m_A[0][1] =  2;
    m_A[1][0] =  2;
    m_A[1][1] =  6;

    m_B[0]    =  2;
    m_B[1]    = -8;
    m_Negate = false;
    }

  double GetValue( const ParametersType & parameters ) const override
    {

    VectorType v( parameters.Size() );
    for(unsigned int i=0; i<SpaceDimension; i++)
      {
      v[i] = parameters[i];
      }
    VectorType Av = m_A * v;
    double val = ( inner_product<double>( Av , v ) )/2.0;
    val -= inner_product< double >( m_B , v );
    if( m_Negate )
      {
      val *= -1.0;
      }
    return val;
    }

  void GetDerivative( const ParametersType & parameters,
                            DerivativeType & derivative ) const override
    {

    VectorType v( parameters.Size() );
    for(unsigned int i=0; i<SpaceDimension; i++)
      {
      v[i] = parameters[i];
      }
    std::cout << "GetDerivative( " << v << " ) = ";
    VectorType gradient = m_A * v  - m_B;
    std::cout << gradient << std::endl;
    derivative = DerivativeType(SpaceDimension);
    for(unsigned int i=0; i<SpaceDimension; i++)
      {
      if( !m_Negate )
        {
        derivative[i] = gradient[i];
        }
      else
        {
        derivative[i] = -gradient[i];
        }
      }
    }

  unsigned int GetNumberOfParameters() const override
    {
    return SpaceDimension;
    }

  // Used to switch between maximization and minimization.
  void SetNegate(bool flag )
    {
    m_Negate = flag;
    }

private:
  MatrixType        m_A;
  VectorType        m_B;
  bool              m_Negate;
};


/**
 * Function we want to optimize, comprised of two parabolas with C0 continuity
 * at 0:
 * f(x) = if(x<0) x^2+4x; else 2x^2-8x
 *
 * Minima are at -2 and 2 with function values of -4 and -8 respectively.
 */
class amoebaTestF2 : public itk::SingleValuedCostFunction
{
public:

  using Self = amoebaTestF2;
  using Superclass = itk::SingleValuedCostFunction;
  using Pointer = itk::SmartPointer<Self>;
  using ConstPointer = itk::SmartPointer<const Self>;
  itkNewMacro( Self );
  itkTypeMacro( amoebaTestF1, SingleValuedCostFunction );

  using ParametersType = Superclass::ParametersType;
  using MeasureType = Superclass::MeasureType;

  amoebaTestF2() = default;

  double GetValue( const ParametersType & parameters ) const override
    {
    double val;
    if( parameters[0]<0 )
      {
      val = parameters[0]*parameters[0]+4*parameters[0];
      }
    else
      {
      val = 2*parameters[0]*parameters[0]-8*parameters[0];
      }
    return val;
    }

  void GetDerivative( const ParametersType & itkNotUsed(parameters),
                            DerivativeType & itkNotUsed(derivative) ) const override
    {
      throw itk::ExceptionObject( __FILE__, __LINE__,
                                  "no derivative available" );
    }

  unsigned int GetNumberOfParameters() const override
    {
    return 1;
    }
};

class CommandIterationUpdateAmoeba : public itk::Command
{
public:
  using Self = CommandIterationUpdateAmoeba;
  using Superclass = itk::Command;
  using Pointer = itk::SmartPointer<Self>;
  itkNewMacro( Self );

  void Reset() { m_IterationNumber = 0; }

  void Execute(itk::Object *caller, const itk::EventObject & event) override
    {
      Execute( (const itk::Object *)caller, event);
    }

  void Execute(const itk::Object * object, const itk::EventObject & event) override
    {
    const auto * optimizer = static_cast< const itk::AmoebaOptimizer * >( object );
    if( dynamic_cast< const itk::FunctionEvaluationIterationEvent * >( &event ) )
      {
      std::cout << m_IterationNumber++ << ":  ";
      std::cout << "x: "<< optimizer->GetCachedCurrentPosition() <<"  ";
      std::cout << "f(x): " << optimizer->GetCachedValue() <<std::endl;

      }
    }

protected:
  CommandIterationUpdateAmoeba()
  {
    m_IterationNumber=0;
  }

private:
  unsigned long m_IterationNumber;
};

/**
 * Test Amoeba with a 2D quadratic function - happy day scenario.
 */
int AmoebaTest1();

/**
 * Test Amoeba and Amoeba with restarts on a function with two minima.
 */
int AmoebaTest2();

int itkAmoebaOptimizerTest(int, char* [] )
{
  int result1 = AmoebaTest1();
  int result2 = AmoebaTest2();

  std::cout<< "All Tests Completed."<< std::endl;

  if( result1 == EXIT_FAILURE ||
      result2 == EXIT_FAILURE )
    {
    std::cerr<<"[FAILURE]\n";
    return EXIT_FAILURE;
    }
  std::cout<<"[SUCCESS]\n";
  return EXIT_SUCCESS;
}

int AmoebaTest1()
{

  std::cout << "Amoeba Optimizer Test 1\n \n";

  using OptimizerType = itk::AmoebaOptimizer;

  // Declaration of a itkOptimizer
  OptimizerType::Pointer  itkOptimizer = OptimizerType::New();

  // set optimizer parameters
  itkOptimizer->SetMaximumNumberOfIterations( 10 );

  double xTolerance = 0.01;
  itkOptimizer->SetParametersConvergenceTolerance( xTolerance );

  double fTolerance = 0.001;
  itkOptimizer->SetFunctionConvergenceTolerance( fTolerance );

  amoebaTestF1::Pointer costFunction = amoebaTestF1::New();
  itkOptimizer->SetCostFunction( costFunction );
  std::cout << "itkOptimizer->GetCostFunction(): " << itkOptimizer->GetCostFunction() << std::endl;

  OptimizerType::ParametersType initialValue(2);       // constructor requires vector size

  initialValue[0] =  100;             // We start not far from  | 2 -2 |
  initialValue[1] = -100;

  OptimizerType::ParametersType currentValue(2);

  currentValue = initialValue;

  itkOptimizer->SetInitialPosition( currentValue );


  try
    {

    std::cout << "Run for " << itkOptimizer->GetMaximumNumberOfIterations();
    std::cout << " iterations or less." << std::endl;

    itkOptimizer->StartOptimization();


    itkOptimizer->SetMaximumNumberOfIterations( 100 );
    std::cout << "Continue for " << itkOptimizer->GetMaximumNumberOfIterations();
    std::cout << " iterations or less." << std::endl;
    itkOptimizer->SetInitialPosition( itkOptimizer->GetCurrentPosition() );
    itkOptimizer->StartOptimization();

    }
  catch( itk::ExceptionObject & e )
    {
    std::cerr << "Exception thrown ! " << std::endl;
    std::cerr << "An error occurred during Optimization" << std::endl;
    std::cerr << "Location    = " << e.GetLocation()    << std::endl;
    std::cerr << "Description = " << e.GetDescription() << std::endl;
    std::cerr <<"[TEST 1 FAILURE]\n";
    return EXIT_FAILURE;
    }


  std::cout << "Optimizer: " << itkOptimizer;

  //
  // check results to see if it is within range
  //

  OptimizerType::ParametersType finalPosition;
  finalPosition = itkOptimizer->GetCurrentPosition();

  double trueParameters[2] = { 2, -2 };
  bool pass = true;

  std::cout << "Right answer   = " << trueParameters[0] << " , " << trueParameters[1] << std::endl;
  std::cout << "Final position = " << finalPosition     << std::endl;

  for( unsigned int j = 0; j < 2; j++ )
    {
    if( itk::Math::abs( finalPosition[j] - trueParameters[j] ) > xTolerance )
      pass = false;
    }

  if( !pass )
    {
    std::cerr<<"[TEST 1 FAILURE]\n";
    return EXIT_FAILURE;
    }

  // Get the final value of the optimizer
  std::cout << "Testing optimizers GetValue() : ";
  OptimizerType::MeasureType finalValue = itkOptimizer->GetValue();
  if(std::fabs(finalValue+9.99998)>0.01)
    {
    std::cerr << "failed\n";
    std::cerr<<"[TEST 1 FAILURE]\n";
    return EXIT_FAILURE;
    }
  else
    {
    std::cout << "succeeded\n";
    }

  // Set now the function to maximize
  //
  { // add a block-scope to have local variables

  std::cout << "Testing Maximization " << std::endl;

  currentValue = initialValue;

  itkOptimizer->SetInitialPosition( currentValue );

  CommandIterationUpdateAmoeba::Pointer observer =
    CommandIterationUpdateAmoeba::New();
  itkOptimizer->AddObserver( itk::FunctionEvaluationIterationEvent(), observer );

  try
    {
    // These two following statement should compensate each other
    // and allow us to get to the same result as the test above.
    costFunction->SetNegate(true);
    itkOptimizer->MaximizeOn();

    std::cout << "Run for " << itkOptimizer->GetMaximumNumberOfIterations();
    std::cout << " iterations or less." << std::endl;

    itkOptimizer->StartOptimization();

    itkOptimizer->SetMaximumNumberOfIterations( 100 );
    itkOptimizer->SetInitialPosition( itkOptimizer->GetCurrentPosition() );

    std::cout << "Continue for " << itkOptimizer->GetMaximumNumberOfIterations();
    std::cout << " iterations or less, starting from previous position.";
    std::cout << std::endl;
    itkOptimizer->StartOptimization();

    }
  catch( itk::ExceptionObject & e )
    {
    std::cerr << "Exception thrown ! " << std::endl;
    std::cerr << "An error occurred during Optimization" << std::endl;
    std::cerr << "Location    = " << e.GetLocation()    << std::endl;
    std::cerr << "Description = " << e.GetDescription() << std::endl;
    std::cerr <<"[TEST 1 FAILURE]\n";
    return EXIT_FAILURE;
    }

  finalPosition = itkOptimizer->GetCurrentPosition();
  std::cout << "Right answer   = " << trueParameters[0] << " , " << trueParameters[1] << std::endl;
  std::cout << "Final position = " << finalPosition     << std::endl;

  for( unsigned int j = 0; j < 2; j++ )
    {
    if( itk::Math::abs( finalPosition[j] - trueParameters[j] ) > xTolerance )
      pass = false;
    }

  if( !pass )
    {
    std::cerr<<"[TEST 1 FAILURE]\n";
    return EXIT_FAILURE;
    }

  // Get the final value of the optimizer
  std::cout << "Testing optimizer's GetValue() [invokes additional function evaluation]: ";
  finalValue = itkOptimizer->GetValue();
  if(std::fabs(finalValue+9.99998)>0.01)
    {
    std::cerr << "failed\n";
    std::cerr <<"[TEST 1 FAILURE]\n";
    return EXIT_FAILURE;
    }
  else
    {
    std::cout << "succeeded\n";
    }

  }
  std::cout<<"[TEST 1 SUCCESS]\n";
  return EXIT_SUCCESS;
}

int AmoebaTest2()
{
  std::cout << "Amoeba Optimizer Test 2\n \n";

  using OptimizerType = itk::AmoebaOptimizer;
  OptimizerType::Pointer  itkOptimizer = OptimizerType::New();

         // set optimizer parameters
  unsigned int maxIterations = 100;
  itkOptimizer->SetMaximumNumberOfIterations( maxIterations );

  double xTolerance = 0.01;
  itkOptimizer->SetParametersConvergenceTolerance( xTolerance );

  double fTolerance = 0.001;
  itkOptimizer->SetFunctionConvergenceTolerance( fTolerance );

          //the initial simplex is constructed as:
          //x,
          //x_i = [x[0], ... , x[i]+initialSimplexDelta[i], ... , x[n]]
          //
  OptimizerType::ParametersType initialSimplexDelta( 1 );
  initialSimplexDelta[0] = 10;
  itkOptimizer->SetInitialSimplexDelta( initialSimplexDelta );

  OptimizerType::ParametersType initialParameters( 1 ), finalParameters;
             //starting position
  initialParameters[0] =  -100;

  itkOptimizer->SetInitialPosition( initialParameters );

              //the function we want to optimize
  amoebaTestF2::Pointer costFunction = amoebaTestF2::New();
  itkOptimizer->SetCostFunction( costFunction );

              //observe the iterations
  CommandIterationUpdateAmoeba::Pointer observer =
    CommandIterationUpdateAmoeba::New();
  itkOptimizer->AddObserver( itk::IterationEvent(), observer );

  try
    {
    itkOptimizer->StartOptimization();
    }
  catch( itk::ExceptionObject & e )
    {
    std::cerr << "Exception thrown ! " << std::endl;
    std::cerr << "An error occurred during Optimization" << std::endl;
    std::cerr << "Location    = " << e.GetLocation()    << std::endl;
    std::cerr << "Description = " << e.GetDescription() << std::endl;
    std::cerr <<"[TEST 2 FAILURE]\n";
    return EXIT_FAILURE;
    }

            //we should have converged to the local minimum, -2
  finalParameters = itkOptimizer->GetCurrentPosition();
  double knownParameters = -2.0;
  std::cout<<"Standard Amoeba:\n";
  std::cout << "Known parameters   = " << knownParameters<<"   ";
  std::cout << "Estimated parameters = " << finalParameters << std::endl;
  std::cout<< "Converged to local minimum." << std::endl;
  if( fabs( finalParameters[0] - knownParameters ) > xTolerance )
    {
    std::cerr<<"[TEST 2 FAILURE]\n";
    return EXIT_FAILURE;
    }

            //run again using multiple restarts
  observer->Reset();
  itkOptimizer->SetInitialPosition( initialParameters );
  itkOptimizer->OptimizeWithRestartsOn();

  try
    {
    itkOptimizer->StartOptimization();
    }
  catch( itk::ExceptionObject & e )
    {
    std::cerr << "Exception thrown ! " << std::endl;
    std::cerr << "An error occurred during Optimization" << std::endl;
    std::cerr << "Location    = " << e.GetLocation()    << std::endl;
    std::cerr << "Description = " << e.GetDescription() << std::endl;
    std::cerr <<"[TEST 2 FAILURE]\n";
    return EXIT_FAILURE;
    }

            //we should have converged to the global minimum, 2
  finalParameters = itkOptimizer->GetCurrentPosition();
  knownParameters = 2.0;
  std::cout<<"Amoeba with restarts:\n";
  std::cout << "Known parameters   = " << knownParameters<<"   ";
  std::cout << "Estimated parameters = " << finalParameters << std::endl;
  std::cout<< "Converged to global minimum." << std::endl;

  if( fabs( finalParameters[0] - knownParameters ) > xTolerance )
    {
    std::cerr <<"[TEST 2 FAILURE]\n";
    return EXIT_FAILURE;
    }
  std::cout<<"[TEST 1 SUCCESS]\n";
  return EXIT_SUCCESS;
}
